Optimization of wind power plant sizing and placement by the application of multi-objective genetic algorithm (GA) in Madhya Pradesh, India

Wind energy generation is a fast developing efficient renewable source widely used for modern power generation. [1] Countries and energy companies have increased their interest in electricity generation from wind energy in the last decades, thanks to its natural, cost-free, and environmentally friendly features. [2] Wind power plants can generate electrical energy at any time of the day and are distributed energy sources for systems that need permanent energy. [3] Nonetheless, due to the disordered nature of the earth's atmosphere, there is a major uncertainty in the electrical power produced by the wind, which causes complexities in the utilization and management of wind energy sources. [4] The changes in weather conditions depending on time and location also create uncertainty in managing the supply-demand balance controlled by the grid operators. [5] In cases where the electrical energy produced by wind power plants is insufficient, auxiliary energy sources provide operational support to the electric grid. [6] Ensuring the supply-demand balance in the electricity grid is also an important condition for the electrical power quality and energy supply in the transmission system, and wind power plant operators care about the follow-up of wind power in terms of economy, operation, and planning. [7] For this, the presence of wind power forecasting (WPF) is an essential factor in the optimization of wind power plants. [8] For consumers to use electrical energy uninterruptedly, efficiently, and economically, wind power plant operators need to estimate the generated electrical energy smoothly. [9] The smooth estimation of electrical power generated by wind turbines increases the operational efficiency for system maintenance, planning, investment, and energy traders. [10,11] It is a precious procedure for electrical grids to provide a respectable commercial yield on the electrical energy market. [12] The capability of advanced WPF provides the contributions like balancing the power system and mitigation of penalties, scheduling the maintenance of wind power plants, investment regarding site location, and technoeconomic and socioeconomic benefits. [13,14] In this way, it is possible to reduce the energy cost and obtain high revenue in the energy market as a result of the contributions obtained for wind power generation systems. [15]

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“…[ 118 ] In addition, efficient placement of wind turbines allows to expand the generation capacity of the system. [ 119 ]…”

Section: Impacts On System Operationmentioning

confidence: 99%

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

et al. 2022

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“…For this reason, a lot of progress has been made in the optimization of photovoltaic systems . We have, for example, metaheuristic methods for searching for MPP, such as the MPPT algorithm, genetic algorithms (Verma et al, 2021), particle swarm optimization (Maleki and Pourfayaz, 2015), the cuckoo search, and the whale optimization algorithm (Maleki and Pourfayaz, 2015). However, improvement in the conditions of use of PV energy remains insufficient.…”

Section: Latitudementioning

confidence: 99%

An Experimental and Case Study on the Evaluation of the Partial Shading Impact on PV Module Performance Operating Under the Sudano-Sahelian Climate of Cameroon

et al. 2022

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Partial shading is a factor that influences the performance of a PV module. The study sought to evaluate the impact of partial shading on PV module performance in the Sudano-Sahelian climate conditions of Cameroon. The behavior of the PV module was simulated using MATLAB/Simulink for 12 months with data from the town of Yagoua. The power, current, and voltage losses of the PV module were estimated by varying the partial shading rate from 5.0% to 95.0%, with an increase factor of 5.0%. The results show that, when the shading ranges from 5.0% to 55.0%, the power and current losses are very significant and vary from 3.0% to 52.0% and 3.0%–53.0%, respectively. The voltage in this shading range remains almost invariant. For shading from 60.0% to 95.0%, the power losses increase slightly and reach approximately 60.0%. A very small current loss is observed, varying from 1.0% to 3.0%. Significant voltage losses are noted and vary from 55.0% to 59.0%. From 40.0% shading rate onwards, a mismatch is observed on the power-voltage characteristics curve by the presence of two maximum power points. This method can be used to evaluate the efficiency of different PV array topologies under partial shading. The results show the importance of paying attention to partial shading, however small its occurrence.

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“…The GA is applied considering two variables which are rated kW of BESS (𝑃 𝑟𝑎𝑡𝑒𝑑 ) and rated kWh of the BESS (𝐽 𝑟𝑎𝑡𝑒𝑑 ). All the works in [3][4][5][6][7][8][9][10][11][12], considered two variables optimizing the cost of the BESS size. We consider two variables while applying GA to compare the result obtained by our proposed technique which considers rated kWh as the sole variable.…”

Section: Application Of the Genetic Algorithm (Ga) In The Bess Size S...mentioning

confidence: 99%

“…These studies focused on capacity optimization for all of the microgrid's sources, including the BESSs. GA [9], Bat algorithm [10], MILP [11], and particle swarm optimization algorithm [12] are some of the optimization approaches employed. The forecasted generation error acts as the input for the BESS size selection.…”

Section: Introductionmentioning

confidence: 99%

A heuristic-iterative technique for optimal battery size selection towards dispatchable wind generation

Kashyap

Ghose

2022

Preprint

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This paper aims to develop a simple yet effective technique for estimating the size of a Battery Energy Storage System (BESS) in order to make a Wind Energy System (WES) work as a dispatchable unit in unit commitment problem. The technique proposes an approximate method that can estimate the initial kWh of the battery and then upgrade the kWh size of the battery iteratively on the basis of heuristic rules that can mitigate the probabilistic forecasted error of wind power generation. An approximate method for initial size of the BESS has been proposed based on the longest continuous discharging cycle of the BESS to obtain near-optimum solution. After determining the initial BESS size, two heuristic rules are used to update the initial value in each iteration when the constraints are violated. A series of probabilistic forecasted wind power generation errors generated by the Autoregressive Dynamic Adaptive (ARDA) technique and then mapped on the Normal Distribution Curve (NDC) for each time block of a day to generate a more appropriate error or load cycle of the BESS to deal with wind speed variability. To demonstrate the justification of the proposed technique and the cost of the BESS, different load cycles have been generated using mean and 1σ of the NDC values. The proposed iterative technique was validated by comparing it to the Genetic Algorithm (GA) when applied to the BESS sizing optimization problem.

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Optimization of wind power plant sizing and placement by the application of multi-objective genetic algorithm (GA) in Madhya Pradesh, India

Cited by 15 publications

References 36 publications

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

An Experimental and Case Study on the Evaluation of the Partial Shading Impact on PV Module Performance Operating Under the Sudano-Sahelian Climate of Cameroon

A heuristic-iterative technique for optimal battery size selection towards dispatchable wind generation

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